


"""simple docstring"""



import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel







class    __a						:







				def __init__( self      ,	UpperCAmelCase      ,	UpperCAmelCase=99      ,	UpperCAmelCase=13      ,	UpperCAmelCase=7      ,	UpperCAmelCase=9      ,	UpperCAmelCase=True      ,	UpperCAmelCase=True      ,	UpperCAmelCase=False      ,	UpperCAmelCase=32      ,	UpperCAmelCase=5      ,	UpperCAmelCase=4      ,	UpperCAmelCase=37      ,	UpperCAmelCase=8      ,	UpperCAmelCase=0.1      ,	UpperCAmelCase=0.0_0_2      ,	UpperCAmelCase=1      ,	UpperCAmelCase=0      ,	UpperCAmelCase=0      ,	UpperCAmelCase=None      ,	UpperCAmelCase=None      ,	):

					'''simple docstring'''






					lowerCAmelCase_      							=			parent
					lowerCAmelCase_      							=			batch_size
					lowerCAmelCase_      							=			encoder_seq_length
					lowerCAmelCase_      							=			decoder_seq_length
					# For common tests
					lowerCAmelCase_      							=			self.decoder_seq_length
					lowerCAmelCase_      							=			is_training
					lowerCAmelCase_      							=			use_attention_mask
					lowerCAmelCase_      							=			use_labels
					lowerCAmelCase_      							=			vocab_size
					lowerCAmelCase_      							=			hidden_size
					lowerCAmelCase_      							=			num_hidden_layers
					lowerCAmelCase_      							=			num_attention_heads
					lowerCAmelCase_      							=			d_ff
					lowerCAmelCase_      							=			relative_attention_num_buckets
					lowerCAmelCase_      							=			dropout_rate
					lowerCAmelCase_      							=			initializer_factor
					lowerCAmelCase_      							=			eos_token_id
					lowerCAmelCase_      							=			pad_token_id
					lowerCAmelCase_      							=			decoder_start_token_id
					lowerCAmelCase_      							=			None
					lowerCAmelCase_      							=			decoder_layers







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					return TaConfig.from_pretrained('''google/umt5-base'''   )







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase=None      ,	UpperCAmelCase=None      ,	UpperCAmelCase=None      ,	UpperCAmelCase=None      ,	UpperCAmelCase=None      ,	):

					'''simple docstring'''






					if attention_mask is None:
						lowerCAmelCase_      							=			input_ids.ne(config.pad_token_id   )
					if decoder_attention_mask is None:
						lowerCAmelCase_      							=			decoder_input_ids.ne(config.pad_token_id   )
					if head_mask is None:
						lowerCAmelCase_      							=			torch.ones(config.num_hidden_layers      ,	config.num_attention_heads      ,	device=UpperCAmelCase   )
					if decoder_head_mask is None:
						lowerCAmelCase_      							=			torch.ones(config.num_decoder_layers      ,	config.num_attention_heads      ,	device=UpperCAmelCase   )
					if cross_attn_head_mask is None:
						lowerCAmelCase_      							=			torch.ones(
						    config.num_decoder_layers      ,	config.num_attention_heads      ,	device=UpperCAmelCase   )
					return {
					    "input_ids": input_ids,
					    "decoder_input_ids": decoder_input_ids,
					    "attention_mask": attention_mask,
					    "decoder_attention_mask": decoder_attention_mask,
					    "head_mask": head_mask,
					    "decoder_head_mask": decoder_head_mask,
					    "cross_attn_head_mask": cross_attn_head_mask,
					}







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			ids_tensor([self.batch_size, self.encoder_seq_length]      ,	self.vocab_size   )
					lowerCAmelCase_      							=			ids_tensor([self.batch_size, self.decoder_seq_length]      ,	self.vocab_size   )

					# we need to clamp the input ids here to avoid having pad token in between
					# this is because for NllbMoe the position_ids are prepared such that
					# all pad tokens have pos id = 2 and rest are between 2..seq_length
					# and the seq_length here is seq_length - num_pad_tokens
					# but when using past, there is no way of knowing if the past input ids had
					# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
					# position_ids being off by num_pad_tokens in past input
					lowerCAmelCase_      							=			input_ids.clamp(self.pad_token_id + 1   )
					lowerCAmelCase_      							=			decoder_input_ids.clamp(self.pad_token_id + 1   )

					lowerCAmelCase_      							=			self.get_config()
					lowerCAmelCase_      							=			config.num_attention_heads
					lowerCAmelCase_      							=			self.prepare_inputs_dict(UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase   )
					return config, input_dict







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_							,	lowerCAmelCase_      							=			self.prepare_config_and_inputs()
					return config, inputs_dict







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					return TaConfig(
					    vocab_size=166      ,	d_model=self.hidden_size      ,	d_ff=self.d_ff      ,	d_kv=self.hidden_size // self.num_attention_heads      ,	num_layers=self.num_hidden_layers      ,	num_decoder_layers=self.decoder_layers      ,	num_heads=self.num_attention_heads      ,	relative_attention_num_buckets=self.relative_attention_num_buckets      ,	dropout_rate=self.dropout_rate      ,	initializer_factor=self.initializer_factor      ,	eos_token_id=self.eos_token_id      ,	bos_token_id=self.pad_token_id      ,	pad_token_id=self.pad_token_id      ,	decoder_start_token_id=self.decoder_start_token_id      ,	)







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					return TaConfig(
					    vocab_size=self.vocab_size      ,	d_model=self.hidden_size      ,	d_ff=self.d_ff      ,	d_kv=self.hidden_size // self.num_attention_heads      ,	num_layers=self.num_hidden_layers      ,	num_decoder_layers=self.decoder_layers      ,	num_heads=self.num_attention_heads      ,	relative_attention_num_buckets=self.relative_attention_num_buckets      ,	dropout_rate=self.dropout_rate      ,	initializer_factor=self.initializer_factor      ,	eos_token_id=self.eos_token_id      ,	bos_token_id=self.pad_token_id      ,	pad_token_id=self.pad_token_id      ,	decoder_start_token_id=self.decoder_start_token_id      ,	)







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	):

					'''simple docstring'''






					lowerCAmelCase_      							=			UMTaModel(config=UpperCAmelCase   )
					model.to(UpperCAmelCase   )
					model.eval()
					lowerCAmelCase_      							=			model(
					    input_ids=UpperCAmelCase      ,	decoder_input_ids=UpperCAmelCase      ,	attention_mask=UpperCAmelCase      ,	decoder_attention_mask=UpperCAmelCase      ,	)
					lowerCAmelCase_      							=			model(input_ids=UpperCAmelCase      ,	decoder_input_ids=UpperCAmelCase   )
					lowerCAmelCase_      							=			result.last_hidden_state
					lowerCAmelCase_      							=			result.past_key_values
					lowerCAmelCase_      							=			result.encoder_last_hidden_state

					self.parent.assertEqual(encoder_output.size()      ,	(self.batch_size, self.encoder_seq_length, self.hidden_size)   )
					self.parent.assertEqual(decoder_output.size()      ,	(self.batch_size, self.decoder_seq_length, self.hidden_size)   )
					# There should be `num_layers` key value embeddings stored in decoder_past
					self.parent.assertEqual(len(UpperCAmelCase   )      ,	config.num_layers   )
					# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
					self.parent.assertEqual(len(decoder_past[0]   )      ,	4   )







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	):

					'''simple docstring'''






					lowerCAmelCase_      							=			UMTaModel(config=UpperCAmelCase   ).get_decoder().to(UpperCAmelCase   ).eval()
					# first forward pass
					lowerCAmelCase_      							=			model(UpperCAmelCase      ,	use_cache=UpperCAmelCase   )
					lowerCAmelCase_      							=			model(UpperCAmelCase   )
					lowerCAmelCase_      							=			model(UpperCAmelCase      ,	use_cache=UpperCAmelCase   )

					self.parent.assertTrue(len(UpperCAmelCase   ) == len(UpperCAmelCase   )   )
					self.parent.assertTrue(len(UpperCAmelCase   ) == len(UpperCAmelCase   ) + 1   )

					lowerCAmelCase_							,	lowerCAmelCase_      							=			outputs.to_tuple()

					# create hypothetical next token and extent to next_input_ids
					lowerCAmelCase_      							=			ids_tensor((self.batch_size, 1)      ,	config.vocab_size   )

					# append to next input_ids and
					lowerCAmelCase_      							=			torch.cat([input_ids, next_tokens]      ,	dim=-1   )

					lowerCAmelCase_      							=			model(UpperCAmelCase   )['''last_hidden_state''']
					lowerCAmelCase_      							=			model(UpperCAmelCase      ,	past_key_values=UpperCAmelCase   )['''last_hidden_state''']

					# select random slice
					lowerCAmelCase_      							=			ids_tensor((1,)      ,	output_from_past.shape[-1]   ).item()
					lowerCAmelCase_      							=			output_from_no_past[:, -1, random_slice_idx].detach()
					lowerCAmelCase_      							=			output_from_past[:, 0, random_slice_idx].detach()

					# test that outputs are equal for slice
					self.parent.assertTrue(torch.allclose(UpperCAmelCase      ,	UpperCAmelCase      ,	atol=1E-3   )   )







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	):

					'''simple docstring'''






					lowerCAmelCase_      							=			UMTaModel(config=UpperCAmelCase   ).to(UpperCAmelCase   ).half().eval()
					lowerCAmelCase_      							=			model(**UpperCAmelCase   )['''last_hidden_state''']
					self.parent.assertFalse(torch.isnan(UpperCAmelCase   ).any().item()   )







@require_torch
class    __a						( __snake_case					,			__snake_case					,			__snake_case					,			unittest.TestCase							):
				lowerCamelCase     :       List[str]			       =(
				    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
				)
				lowerCamelCase     :       Dict			       =(UMTaForConditionalGeneration,) if is_torch_available() else ()
				lowerCamelCase     :       int			       =(
				    {
				        'conversational': UMTaForConditionalGeneration,
				        'feature-extraction': UMTaModel,
				        'summarization': UMTaForConditionalGeneration,
				        'text2text-generation': UMTaForConditionalGeneration,
				        'translation': UMTaForConditionalGeneration,
				        'question-answering': UMTaForQuestionAnswering,
				    }
				    if is_torch_available()
				    else {}
				)
				lowerCamelCase     :       List[str]			       =True
				lowerCamelCase     :       int			       =False
				lowerCamelCase     :       List[Any]			       =False
				lowerCamelCase     :       str			       =True
				lowerCamelCase     :       Optional[int]			       =True
				# The small UMT5 model needs higher percentages for CPU/MP tests
				lowerCamelCase     :       Dict			       =[0.8, 0.9]







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			UMTaModelTester(self   )







				@unittest.skip('''Test has a segmentation fault on torch 1.8.0'''   )
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.model_tester.prepare_config_and_inputs()
					lowerCAmelCase_      							=			UMTaModel(config_and_inputs[0]   ).to(UpperCAmelCase   )
					with tempfile.TemporaryDirectory() as tmpdirname:
						torch.onnx.export(
						    UpperCAmelCase      ,	(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])      ,	F"""{tmpdirname}/t5_test.onnx"""      ,	export_params=UpperCAmelCase      ,	opset_version=9      ,	input_names=['''input_ids''', '''decoder_input_ids''']      ,	)







				@unittest.skipIf(torch_device == '''cpu'''      ,	'''Cant do half precision'''   )
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model_fpaa_forward(*UpperCAmelCase   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
					lowerCAmelCase_      							=			self.model_tester.prepare_config_and_inputs()
					lowerCAmelCase_      							=			config_and_inputs[0]
					lowerCAmelCase_      							=			UMTaForConditionalGeneration(UpperCAmelCase   ).eval()
					model.to(UpperCAmelCase   )

					lowerCAmelCase_      							=			{
					    '''head_mask''': torch.zeros(config.num_layers      ,	config.num_heads      ,	device=UpperCAmelCase   ),
					    '''decoder_head_mask''': torch.zeros(config.num_decoder_layers      ,	config.num_heads      ,	device=UpperCAmelCase   ),
					    '''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers      ,	config.num_heads      ,	device=UpperCAmelCase   ),
					}

					for attn_name, (name, mask) in zip(UpperCAmelCase      ,	head_masking.items()   ):
						lowerCAmelCase_      							=			{name: mask}
						# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
						if name == "head_mask":
							lowerCAmelCase_      							=			torch.ones(
							    config.num_decoder_layers      ,	config.num_heads      ,	device=UpperCAmelCase   )

						lowerCAmelCase_      							=			model.generate(
						    config_and_inputs[1]['''input_ids''']      ,	num_beams=1      ,	max_length=3      ,	output_attentions=UpperCAmelCase      ,	return_dict_in_generate=UpperCAmelCase      ,	**UpperCAmelCase      ,	)
						# We check the state of decoder_attentions and cross_attentions just from the last step
						lowerCAmelCase_      							=			out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
						self.assertEqual(sum([w.sum().item() for w in attn_weights]   )      ,	0.0   )







				@unittest.skip('''Does not work on the tiny model as we keep hitting edge cases.'''   )
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					pass







@require_torch
@require_sentencepiece
@require_tokenizers
class    __a						( unittest.TestCase							):







				@slow
				@unittest.skip(
				    '''Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged'''   )
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''      ,	return_dict=UpperCAmelCase   ).to(UpperCAmelCase   )
					lowerCAmelCase_      							=			AutoTokenizer.from_pretrained('''google/umt5-small'''      ,	use_fast=UpperCAmelCase      ,	legacy=UpperCAmelCase   )
					lowerCAmelCase_      							=			[
					    '''Bonjour monsieur <extra_id_0> bien <extra_id_1>.''',
					    '''No se como puedo <extra_id_0>.''',
					    '''This is the reason why we <extra_id_0> them.''',
					    '''The <extra_id_0> walks in <extra_id_1>, seats''',
					    '''A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.''',
					]
					lowerCAmelCase_      							=			tokenizer(UpperCAmelCase      ,	return_tensors='''pt'''      ,	padding=UpperCAmelCase   ).input_ids
					# fmt: off
					lowerCAmelCase_      							=			torch.tensor(
					    [
					        [ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
					        [   826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
					        [  1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
					        [   517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
					        [   320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
					    ]   )
					# fmt: on
					torch.testing.assert_allclose(UpperCAmelCase      ,	UpperCAmelCase   )

					lowerCAmelCase_      							=			model.generate(input_ids.to(UpperCAmelCase   )   )
					lowerCAmelCase_      							=			[
					    '''<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>''',
					    '''<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
					    '''<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
					    '''<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
					    '''<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
					]
					lowerCAmelCase_      							=			tokenizer.batch_decode(UpperCAmelCase   )
					self.assertEqual(UpperCAmelCase      ,	UpperCAmelCase   )



"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_							=							logging.get_logger(__name__)

lowercase_							=							{
    'google/mobilenet_v2_1.4_224': 'https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json',
    'google/mobilenet_v2_1.0_224': 'https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json',
    'google/mobilenet_v2_0.75_160': 'https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json',
    'google/mobilenet_v2_0.35_96': 'https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}







class    __a						( __snake_case							):
				lowerCamelCase     :       List[Any]			       ='mobilenet_v2'







				def __init__( self      ,	UpperCAmelCase=3      ,	UpperCAmelCase=224      ,	UpperCAmelCase=1.0      ,	UpperCAmelCase=8      ,	UpperCAmelCase=8      ,	UpperCAmelCase=6      ,	UpperCAmelCase=32      ,	UpperCAmelCase=True      ,	UpperCAmelCase=True      ,	UpperCAmelCase="relu6"      ,	UpperCAmelCase=True      ,	UpperCAmelCase=0.8      ,	UpperCAmelCase=0.0_2      ,	UpperCAmelCase=0.0_0_1      ,	UpperCAmelCase=255      ,	**UpperCAmelCase      ,	):

					'''simple docstring'''






					super().__init__(**UpperCAmelCase   )

					if depth_multiplier <= 0:
						raise ValueError('''depth_multiplier must be greater than zero.'''   )

					lowerCAmelCase_      							=			num_channels
					lowerCAmelCase_      							=			image_size
					lowerCAmelCase_      							=			depth_multiplier
					lowerCAmelCase_      							=			depth_divisible_by
					lowerCAmelCase_      							=			min_depth
					lowerCAmelCase_      							=			expand_ratio
					lowerCAmelCase_      							=			output_stride
					lowerCAmelCase_      							=			first_layer_is_expansion
					lowerCAmelCase_      							=			finegrained_output
					lowerCAmelCase_      							=			hidden_act
					lowerCAmelCase_      							=			tf_padding
					lowerCAmelCase_      							=			classifier_dropout_prob
					lowerCAmelCase_      							=			initializer_range
					lowerCAmelCase_      							=			layer_norm_eps
					lowerCAmelCase_      							=			semantic_loss_ignore_index







class    __a						( __snake_case							):
				lowerCamelCase     :       Optional[int]			       =version.parse('1.11'							)







				@property
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					return OrderedDict([('''pixel_values''', {0: '''batch'''})]   )







				@property
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					if self.task == "image-classification":
						return OrderedDict([('''logits''', {0: '''batch'''})]   )
					else:
						return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]   )







				@property
				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					return 1E-4